Tools

by
Craig A. Knoblock, Yigal Arens, Chun-Nan Hsu
- IN PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COOPERATIVE INFORMATION SYSTEMS, 1994

"... With the vast number of information resources available today, a critical problem is how to locate, retrieve and process information. It would be impractical to build a single unified system that combines all of these information resources. A more promising approach is to build specialized informati ..."

With the vast number of information resources available today, a critical problem is how to locate, retrieve and process information. It would be impractical to build a single unified system that combines all of these information resources. A more promising approach is to build specialized information retrieval agents that provide access to a subset of the information resources and can send requests to other information retrieval agents when appropriate. In this paper we present the architecture of the individual information retrieval agents and describe how this architecture supports a network of cooperating information agents. We describe how these information agents represent their knowledge, communicate with other agents, dynamically construct information retrieval plans, and learn about other agents to improve efficiency. We have already built a small network of agents that have these capabilities and provide access to information for transportation planning.

"... Semantic query optimization can dramatically speed up database query answering byknowledge intensive reformulation. But the problem of how to learn the required semantic rules has not been previously solved. This chapter presents a learning approach to solving this problem. In our approach, the lear ..."

Semantic query optimization can dramatically speed up database query answering byknowledge intensive reformulation. But the problem of how to learn the required semantic rules has not been previously solved. This chapter presents a learning approach to solving this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and attributes to construct rules from a database with many relations. The learned semantic rules are effective for optimization because they will match query patterns and reflect data regularities. Experimental results show that this approach learns sufficient rules for optimization that produces a substantial cost reduction.

by
Chun-Nan Hsu , Craig A. Knoblock
- IN PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 1993

"... A practical heterogeneous, distributed multidatabase system must answer queries efficiently. Conventional query optimization techniques are not adequate here because these techniques are dependent on the database structure, and rely on limited information which is not sufficient in complicated mult ..."

A practical heterogeneous, distributed multidatabase system must answer queries efficiently. Conventional query optimization techniques are not adequate here because these techniques are dependent on the database structure, and rely on limited information which is not sufficient in complicated multidatabase queries. This paper presents an automated approach to reformulating query plans to improve the efficiency of multidatabase queries. This approach uses database abstractions, the knowledge about the contents of databases, to reformulate a query plan into less expensive but semantically equivalent one. We present two algorithms. The first algorithm reformulates subqueries to individual databases, the second algorithm extends the first one and reformulates the entire query plan. Empirical results show that the reformulations can provide significant savings with minimal overhead. The reformulation approach provides a global reduction in the amount of the intermediate data as well as local opt...

"... For semantic query optimization one needs detailed knowledge about the contents of the database. Traditional techniques use static knowledge about all possible states of the database which is already given. New techniques use knowledge only about the current state of the database which can be found ..."

For semantic query optimization one needs detailed knowledge about the contents of the database. Traditional techniques use static knowledge about all possible states of the database which is already given. New techniques use knowledge only about the current state of the database which can be found by methods of knowledge discovery in databases. Databases are often very large and permanently in use. Therefore, methods of knowledge discovery are only allowed to take a small amount of the capacity of the database system. So database access has to be reduced to a minimum during the process of discovery and obviously if the database changes during the process of maintenance of the discovered knowledge. In this paper, the main effort has been put into minimizing the number of database accesses, w.r.t. discovery and maintenance. This is exemplified by the discovery of functional dependencies. We improve the inference of functional dependencies by using independencies and cardinality dependen...

...sent content of the database. Siegel has reported this by the first time (Siegel 1988) and (Siegel, Sciore, & Salveter 1991). Such constraints have been termed, for example, Database Abstractions in (=-=Hsu & Knoblock 1993-=-), Metadata in (Siegel & Madnick 1991), and Meta Knowledge in (Schlimmer 1991). Also, Hsu and Knoblock (Hsu & Knoblock 1993) have shown the benefits of optimization techniques based on automatically d...

"... Abstract: Semantic query optimization uses semantic knowledge to transform a query into another form that can be executed in a more efficient manner but still yields the same result as the original query. The semantic knowledge can be supplied by users or derived by the system.. In this paper, we de ..."

Abstract: Semantic query optimization uses semantic knowledge to transform a query into another form that can be executed in a more efficient manner but still yields the same result as the original query. The semantic knowledge can be supplied by users or derived by the system.. In this paper, we describe the ARDOR semantic query optimizer with automatic rule derivation capabilities which, in recent field trials, has demonstrated significant reductions in query execution time. 1.

... work [3, 10,12,14]. In [7,15,16] the learning approach is driven mainly by a fixed set of heuristics based on the structure and implementation of the database. In contrast, the approach discussed in =-=[9,13,17]-=- places the emphasis on user queries, and data retrieved, to perform the rule identification needed for query reformulation. It is therefore likely to derive more useful rules since it is based on act...

by
Chun-Nan Hsu , Craig A. Knoblock
- IN PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON KNWLEDGE DISCOVERY AND DATA MINING, 1995

"... This paper introduces a new measurement, robustness, to measure the qualityofmachine-discovered knowledge from real-world databases that change over time. A piece of knowledge is robust if it is unlikely to become inconsistent with new database states. Robustness is different from predictive ac ..."

This paper introduces a new measurement, robustness, to measure the qualityofmachine-discovered knowledge from real-world databases that change over time. A piece of knowledge is robust if it is unlikely to become inconsistent with new database states. Robustness is different from predictive accuracy in that by the latter, the system considers only the consistency of a rule with unseen data, while by the former, the consistency after deletions and updates of existing data is also considered. Combining robustness with other utility measurements, a system can makeintelligent decisions in learning and maintenance of knowledge learned from changing databases. This paper defines robustness, then presents an estimation approach for the robustness of Horn-clause rules learned from a relational database. The estimation approach applies the Laplace law of succession, which can be efficiently computed. The estimation is based on database schemas and transaction logs. No domains...

...o as to improve their performance. Ansexample of those applications is learning for semanticsquery optimization (Siegel 1988; Hsu & Knoblock 1994;s1995). Semantic query optimization (SQO) (Kings1981; =-=Hsu & Knoblock 1993-=-b) optimizes a query bysusing semantic rules, such as all Maltese seaports havesrailroad access, to reformulate a query into a less ex-spensive but equivalent query. For example, supposeswe have a que...

"... Abstract sources that are available to it. Given an informa-tion request, an agent identifies an appropriate set of With the vast number of information resources information sources, generates a plan to retrieve and available today, a critical problem is how to lo- process the data, uses knowledge a ..."

Abstract sources that are available to it. Given an informa-tion request, an agent identifies an appropriate set of With the vast number of information resources information sources, generates a plan to retrieve and available today, a critical problem is how to lo- process the data, uses knowledge about the data to re-cate, retrieve and process information. It would formulate the plan, and then executes it. This paper be impractical to build a single unified system that describes our approach to the issues of representation, combines all of these information resources. A communication, problem solving, and learning, and de-more promising approach is to build specialized scribes how this approach supports multiple, collabo-information retrieval agents that provide access rating information retrieval agents. to a subset of the information resources and can send requests to other information retrieval agents Representing the Knowledge of an when needed. In this paper we present an archi-tecture for building such agents that addresses the Agent issues of representation, communication, problem Each information agent is specialized to a particular solving, and learning. We also describe how this area of expertise. This provides a modular organiza-

"... A critical problem in building an information mediator is how to translate a domain-level queries into an efficient query plan for accessing the required data. We have built a flexible and efficient information mediator, called SIMS. SIMS takes a domain-level query and dynamically selects the approp ..."

A critical problem in building an information mediator is how to translate a domain-level queries into an efficient query plan for accessing the required data. We have built a flexible and efficient information mediator, called SIMS. SIMS takes a domain-level query and dynamically selects the appropriate information sources based on their content and availability, generates a query access plan that specifies the operations and their order for processing the data, and then performs semantic query reformulation to minimize the overall execution time. This paper describes these three basic components of the query processing in SIMS. 1 Introduction SIMS [ Arens et al., 1993 ] is an information retrieval system that provides an intelligent mediator between information sources and humans users or applications programs. Queries are expressed in a uniform language, independent of the distribution of information over sources, of the various query languages, the location of sources, etc. SIMS d...

by
Siegfried Bell
- Proc. of the 2nd International Conf. on the Practical Application of Constraint Technology

"... The aim of query optimization is to produce an equivalent query which is less expensive to process than the original query. Semantic query optimization involves the use of semantic knowledge during the optimization process. The success strongly depends on the availability of this knowledge. We prese ..."

The aim of query optimization is to produce an equivalent query which is less expensive to process than the original query. Semantic query optimization involves the use of semantic knowledge during the optimization process. The success strongly depends on the availability of this knowledge. We present a rule--based approach of semantic query optimization which overcomes the limitation of predefined knowledge by discovering constraints. We show that unnecessary distinct--keywords and group by--parts of an SQL query can be detected and removed with the use of discovered constraints. 1 Introduction Semantic query optimization (SQO) provides the same kind of transparency with respect to semantic knowledge like relational optimizers do with respect to physical representation. Since semantically equivalent queries can differ significantly in their evaluation costs, it is our major goal to free the user from finding the most effective query. Finding semantically equivalent queries requires s...